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In probabilistic classification, a discriminative model based on the softmax function has a potential limitation in that it assumes unimodality for each class in the feature space.
On the convergence properties of the EM algorithm
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Discriminative training of Gaussian mixture models for large vocabulary speech recognition systems
Lalit R Bahl, Mukund Padmanabhan, David Nahamoo, and PS Gopalakrishnan · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
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A log-linearized Gaussian mixture network and its application to EEG pattern classification
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Analysis of sparse Bayesian learning
Anita Faul and Michael Tipping · 2001
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Soft margins for adaboost
Gunnar Rätsch, Takashi Onoda, and K-R Müller · 2001
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Discriminative training of Gaussian mixture bigram models with application to Chinese dialect identification
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Discriminative Gaussian mixture models: A comparison with kernel classifiers
Aldebaro Klautau, Nikola Jevtic, and Alon Orlitsky · 2003
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Integrating Gaussian mixtures into deep neural networks: Softmax layer with hidden variables
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A Gaussian mixture model layer jointly optimized with discriminative features within a deep neural network architecture
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Large-margin softmax loss for convolutional neural networks
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
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Densely connected convolutional networks
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